Brasilidade: Measuring Brazilian Identity for Large Language Models

Computer Science > Computation and Language

[Submitted on 15 Oct 2026]

Title:Brasilidade: Measuring Brazilian Identity for Large Language Models

View PDF HTML (experimental)Abstract:This work presents a new benchmark that aims to address the limitations of current methods for evaluating LLMs in Brazilian Portuguese. We introduced Brasilidade, an assessment methodology comprised of standardized tasks distributed across four different domains, and we are making available the tasks from its first domain, which address nuances of the language, grammar, and variations of Brazilian Portuguese. The results show that model size, by itself, does not determine performance; several medium-sized models outperformed larger models in the protocols evaluated. Specific models for Portuguese, in general, did not perform above average, despite their targeted training. Different task levels revealed significant nuances: while some tasks proved easy for most models, others posed extreme challenges, and some yielded mixed results, with wide variations in performance. This clearly illustrates the need for benchmarks that address the limitations of the models in Brazilian Portuguese, allowing future training cycles to seek to overcome them. Project repository this https URL

Submission history

From: William Alberto Cruz Castañeda PhD [view email][v1] Thu, 15 Oct 2026 02:14:13 UTC (4,237 KB)

Additional Features

References & Citations

Loading...

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)

Connected Papers (What is Connected Papers?)

Litmaps (What is Litmaps?)

scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub (What is DagsHub?)

Gotit.pub (What is GotitPub?)

Hugging Face (What is Huggingface?)

ScienceCast (What is ScienceCast?)

Demos

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)

CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.